According to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability. Given the complexity and clinical heterogeneity of these disorders, a shift from “canonical treatment” to personalized medicine with improved patient stratification is needed. OPADE is a non-profit study that researches biomarkers in MDD to tailor personalized drug treatments, integrating genetics, epigenetics, microbiome, immune response, and clinical data for analysis. A total of 350 patients between 14 and 50 years will be recruited in 6 Countries (Italy, Colombia, Spain, The Netherlands, Turkey) for 24 months. Real-time electroencephalogram (EEG) and patient cognitive assessment will be correlated with biological sample analysis. A patient empowerment tool will be deployed to ensure patient commitment and to translate patient stories into data. The resulting data will be used to train the artificial intelligence/machine learning (AI/ML) predictive tool.

Background: The integration of digital health technologies has transformed mental healthcare, particularly for young adults with First-Episode Psychosis (FEP). Digital interventions, such as telepsychiatry and mobile applications, address barriers like social stigma, restricted access to services, and the urgency of timely care. Methods: A systematic literature review was conducted using PubMed and APA PsycINFO. Included studies focused on randomized controlled trials, cohort studies, and open studies of digital interventions for FEP rehabilitation. Exclusion criteria included meta-analyses, reviews, and irrelevant studies. Results: Smartphone applications like Horyzons-Canada facilitated peer support and symptom management, while tools such as My Journey 3 emphasized the need for better user engagement. Digital innovations, including Cognitive Behavioral Therapy for insomnia (CBT-I) and telepsychiatry, demonstrated improvements in recovery and treatment adherence. Discussion: Digital interventions significantly enhance the accessibility and quality of FEP care, supporting functional recovery and patient engagement. Future research should prioritize longitudinal studies, user engagement strategies, and advanced analytics to develop personalized, scalable solutions.

Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia’s structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder’s heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI’s integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry.

Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients’ state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders.Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles.Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression.Discussion: Depression’s stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting.Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.

Background: Art therapy is increasingly recognized as a valuable complementary intervention for individuals with eating disorders (EDs), who frequently experience comorbid anxiety and difficulties with emotional regulation. However, few studies have examined its short-term effects on state and trait anxiety within structured clinical settings. Methods: This pilot study involved 19 adolescent females (mean age 17.7 ± 2.1 years) diagnosed with anorexia nervosa (AN) or bulimia nervosa (BN) and admitted to the Mariconda Regional Residence for Eating Disorders (ASL Salerno, Italy) in residential or semi-residential treatment. Participants completed a structured six-week cycle of weekly textile-based art therapy sessions, designed to promote emotional expression and body reconnection. State and trait anxiety levels were assessed pre- and post-session using the State-Trait Anxiety Inventory (STAI). Repeated-measures ANOVA was used to analyze state anxiety changes; a linear mixed-effects model was applied to trait anxiety. Results: State anxiety significantly decreased immediately after sessions (p = 0.002). A significant main effect of session (p = 0.01) and a time × session interaction (p = 0.025) indicated variability across sessions. Trait anxiety showed a non-significant trend toward reduction (p = 0.11); however, reductions were significant at sessions 4 (p = 0.015), 5 (p < 0.001), and 6 (p = 0.005). Conclusions: Art therapy may offer immediate reductions in state anxiety and may contribute to a longer-term reduction in trait anxiety with 4–6 sessions. These findings support integrating creative interventions within multidisciplinary ED treatment programs. Future research with larger samples and control groups is needed to confirm and expand upon these preliminary results.